



import inspect
from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel, VQModel
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


class   __lowerCAmelCase  (		_lowerCAmelCase   ):
					"""simple docstring"""






					def __init__(  self       :							List[Any]					,       _snake_case       :							VQModel					,       _snake_case       :							UNetaDModel					,       _snake_case       :							DDIMScheduler       ):






								"""simple docstring"""


								super().__init__()
								self.register_modules(vqvae=UpperCAmelCase_					,       unet=UpperCAmelCase_					,       scheduler=UpperCAmelCase_       )



					@torch.no_grad()
					def __call__(  self       :							Optional[int]					,       _snake_case       :							int = 1					,       _snake_case       :							Optional[Union[torch.Generator, List[torch.Generator]]] = None					,       _snake_case       :							float = 0.0					,       _snake_case       :							int = 50					,       _snake_case       :							Optional[str] = "pil"					,       _snake_case       :							bool = True					,       **_snake_case       :							List[str]					,       ):






								"""simple docstring"""


								A__	  =     randn_tensor(
								    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)					,       generator=UpperCAmelCase_					,       )
								A__	  =     latents.to(self.device       )

								# scale the initial noise by the standard deviation required by the scheduler
								A__	  =     latents * self.scheduler.init_noise_sigma

								self.scheduler.set_timesteps(UpperCAmelCase_       )

								# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
								A__	  =     'eta' in set(inspect.signature(self.scheduler.step       ).parameters.keys()       )

								A__	  =     {}
								if accepts_eta:
											A__	  =     eta

								for t in self.progress_bar(self.scheduler.timesteps       ):
											A__	  =     self.scheduler.scale_model_input(UpperCAmelCase_					,       UpperCAmelCase_       )
											# predict the noise residual
											A__	  =     self.unet(UpperCAmelCase_					,       UpperCAmelCase_       ).sample
											# compute the previous noisy sample x_t -> x_t-1
											A__	  =     self.scheduler.step(UpperCAmelCase_					,       UpperCAmelCase_					,       UpperCAmelCase_					,       **UpperCAmelCase_       ).prev_sample

								# decode the image latents with the VAE
								A__	  =     self.vqvae.decode(UpperCAmelCase_       ).sample

								A__	  =     (image / 2 + 0.5).clamp(0					,       1       )
								A__	  =     image.cpu().permute(0					,       2					,       3					,       1       ).numpy()
								if output_type == "pil":
											A__	  =     self.numpy_to_pil(UpperCAmelCase_       )

								if not return_dict:
											return (image,)

								return ImagePipelineOutput(images=UpperCAmelCase_       )






"""simple docstring"""


def 						lowerCamelCase__      (				UpperCAmelCase_ = 60_08_51_47_51_43					)->				int:







       """simple docstring"""

       try:
              UpperCamelCase       			=		int(UpperCAmelCase_					)
       except (TypeError, ValueError):
              raise TypeError("Parameter n must be int or castable to int."					)
       if n <= 0:
              raise ValueError("Parameter n must be greater than or equal to one."					)
       UpperCamelCase       			=		2
       UpperCamelCase       			=		0
       if n == 2:
              return 2
       while n > 2:
              while n % i != 0:
                     i += 1
              UpperCamelCase       			=		i
              while n % i == 0:
                     UpperCamelCase       			=		n // i
              i += 1
       return int(UpperCAmelCase_					)


if __name__ == "__main__":
      print(F'''{solution() = }''')





